A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Differentiable pipeline replaces non-differentiable CAE components for shape optimization.
Improved CAEs reduce training time and enhance generalization.
CAE repurposes critics for efficient exploration in RL.
A CAE improves DNN's outlier and adversary defense.
Deep generative models have made tremendous advances in image and signal representation learning and generation. These models employ the full Euclidean space or a bounded subset as the latent space, whose flat geometry, however, is often too simplistic to meaningfully reflect the manifold structure of the data. In this…
This paper presents a learning method for convolutional autoencoders (CAEs) for extracting features from images. CAEs can be obtained by utilizing convolutional neural networks to learn an approximation to the identity function in an unsupervised manner. The loss function based on the pixel loss (PL) that is the mean s…
We compare features for dynamic time warping (DTW) when used to bootstrap keyword spotting (KWS) in an almost zero-resource setting. Such quickly-deployable systems aim to support United Nations (UN) humanitarian relief efforts in parts of Africa with severely under-resourced languages. Our objective is to identify aco…
A scalable framework selects top factors from CAE latent factors for better portfolio optimization.
Convolutional neural networks (CNN) have recently achieved state-of-the-art results in various applications. In the case of image recognition, an ideal model has to learn independently of the training data, both local dependencies between the three components (R,G,B) of a pixel, and the global relations describing edge…
C-Learning estimates reachability over time to solve multi-goal tasks.
Paper tackles robust domain adaptation without target domain data.
Simulation workflow is a top-level model for the design and control of simulation process. It connects multiple simulation components with time and interaction restrictions to form a complete simulation system. Before the construction and evaluation of the component models, the validation of upper-layer simulation work…
Paper presents a workflow for reliable unsupervised learning in science.
This paper analyzes machine learning workflows in climate modeling.
Enhanced autoencoders improve ROMs for PDEs by capturing essential properties.
Adaptive workflow combines fast amortized inference with MCMC for many datasets.
Generative AI agents improve ERP systems by automating complex financial tasks.
Researchers found that avoiding synthetic data generation prevents model collapse in machine learning.
Audit financial machine learning workflows to detect spurious predictability.
The study of genetic variants can help find correlating population groups to identify cohorts that are predisposed to common diseases and explain differences in disease susceptibility and how patients react to drugs. Machine learning algorithms are increasingly being applied to identify interacting GVs to understand th…
A new method for releasing AI workflows to avoid premature incorrect results.
Machine learning workflow development is anecdotally regarded to be an iterative process of trial-and-error with humans-in-the-loop. However, we are not aware of quantitative evidence corroborating this popular belief. A quantitative characterization of iteration can serve as a benchmark for machine learning workflow d…
One of the impediments in advancing actuarial research and developing open source assets for insurance analytics is the lack of realistic publicly available datasets. In this work, we develop a workflow for synthesizing insurance datasets leveraging CTGAN, a recently proposed neural network architecture for generating …
Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary cl…
Deep learning model improves seismic rock property estimation.
Deep learning speeds up pressure prediction in carbon storage reservoirs.
The present study provides a comparative assessment of non-invasive sensors as means of estimating the microbial contamination and time-on-shelf (i.e. storage time) of leafy green vegetables, using a novel unified spectra analysis workflow. Two fresh ready-to-eat green salads were used in the context of this study for …
Unsupervised deep learning detects and localizes crop leaf diseases.
Paper proposes an active learning method for surgical workflow recognition using long-range temporal dependency.
This thesis builds a real-time VaR calculation workflow for crypto derivatives.
The ubiquitous availability of wearable sensors is responsible for driving the Internet-of-Things but is also making an impact on sport sciences and precision medicine. While human activity recognition from smartphone data or other types of inertial measurement units (IMU) has evolved to one of the most prominent daily…
Testing (conditional) independence of multivariate random variables is a task central to statistical inference and modelling in general - though unfortunately one for which to date there does not exist a practicable workflow. State-of-art workflows suffer from the need for heuristic or subjective manual choices, high c…
Benchpress streamlines benchmarking structure learning algorithms for probabilistic models.
Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows -- by modifying the data pre-processing, model training, and post-processing steps -- via trial-and-error to achieve the desired model performance. Existing work on accelerating machine learni…
GSR optimizes tasks in scientific workflows, improving performance across diverse applications.
BayesFlow trains neural networks for fast Bayesian inference.
Foundation models alter medical data science workflow, challenging veridical data science principles.
Workflow improves credit default prediction using machine learning.
A GPU-based workflow for building physics emulators of hypersonic flows
Workflow uses deep learning to improve geosteering accuracy in Goliat Field.
This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabilities. HyperStream overcomes the limitations of other computational engines and provides high-level interfaces to execute complex nesting, fu…
bioLeak addresses data leakage in biomedical machine learning studies.
Qlib aims to integrate AI into quantitative investment.
Predicting the outcome of sales opportunities is a core part of successful business management. Conventionally, making this prediction has relied mostly on subjective human evaluations in the process of sales decision making. In this paper, we addressed the problem of forecasting the outcome of business to business (B2…
Quantifying the importance of each training point to a learning task is a fundamental problem in machine learning and the estimated importance scores have been leveraged to guide a range of data workflows such as data summarization and domain adaption. One simple idea is to use the leave-one-out error of each training …
Automated surgical workflow analysis and understanding can assist surgeons to standardize procedures and enhance post-surgical assessment and indexing, as well as, interventional monitoring. Computer-assisted interventional (CAI) systems based on video can perform workflow estimation through surgical instruments' recog…
AI helps simplify complex ship finance processes.
Unified platform for statistical and machine learning in bioinformatics.